1. Field of the Invention
The present invention relates to a technique of searching for clustering faults in semiconductor device manufacturing. In particular, the present invention relates to a method of searching for clustering faults in semiconductor device manufacturing and designing. The searched clustering faults are used to find and correct abnormalities in manufacturing processes, to improve the productivity of semiconductor devices. The present invention also relates to an apparatus to execute the method and a program to make a computer execute the method. The present invention also relates to applications of the clustering faults searching method, including a method of optimizing the number of redundant circuits in a semiconductor chip, a method of managing manufacturing processes, a method of managing a dean room, a method of manufacturing semiconductor devices, a method of finding problematic processes and equipment, and a method of determining whether or not semifinished products must be scrapped.
2. Description of the Related Art
Recent fine semiconductor devices have severe process margins to easily produce faults due to mismatched masks, uneven impurity concentrations, irregular film thicknesses, fine defects on wafers, etc. There is a need of clarifying the cause of such faults, to correct manufacturing processes accordingly and improve yield.
A process improving technique according to a prior art will be explained. This technique employs a fault map such as a fail bit map and empirically determines whether or not electrical faults found on a wafer are random or collective. The collective faults occurring at a specific location on an object are called “clustering faults.” If the clustering faults are found at, for example, the periphery of a wafer, the location is investigated to estimate a cause of the faults. Misaligned masks may cause clustering electrical faults at the periphery of a wafer. Based on a result of the investigation, manufacturing equipment such as a stepper is examined to find a principal cause of the faults. Another prior art employs a fault observatory system to find faults in a wafer. If faults whose number exceeds an empirical clustering faults threshold are found at a location, the technique investigates the location for a cause.
These techniques rely on user's skill and empirical clustering faults thresholds in studying a fault map and finding clustering faults on the map, and therefore, they lack objectivity and are incapable of quantitatively determine whether or not faults in a wafer are random or clustering due to a specific cause.
Another prior art employing a statistical technique to find clustering faults is disclosed in Proc. 1997 Second Int. Workshop Statistical Metrology, pp. 52-55. This technique prepares a frequency distribution of faults in chips and approximates the frequency distribution with a Poisson distribution. A tail of the Poisson distribution corresponds to large numbers of faults and involves substantially no chips. If the tall involves any number of chips, the prior art determines that there are clustering faults. Any person skilled in the art, however, may find the pence of clustering faults on the Poisson distribution only by seeing it. This prior art provides no technique of quantitatively evaluating the tail shape of a Poisson distribution to determine the presence of clustering faults.
There is a need of providing a statistical technique of discriminating random faults from clustering faults.